This paper considers a multi-user downlink non-orthogonal multiple access (NOMA) framework comprising of an access point (AP), a cluster of N near-users (NUs), and a cluster of F far-users (FUs). The AP shares direct links to both NUs and FUs. Using only a one-bit feedback from the selected users, the network switches between NOMA, cooperative NOMA, and orthogonal multiple access (OMA) modes. Considering channel-aware dynamic power allocation (DPA), closed-form expressions are derived for the outage probability, throughput, and EE for the first time in literature with novel power control, mode selection, and user selection. Further, it is demonstrated that the power allocation with very limited channel state information (CSI) results in a significant gain in throughput and also saves power at AP, thereby increasing EE. We show in that intelligent user selection and use of channel-aware techniques is the key to high spectral efficiency (SE) and energy efficiency (EE). It is shown how the minimum transmit power at the AP and the NU, as well as the best NOMA power-allocation can be determined based on limited channel knowledge to maximize SE and EE. The use of minimum power needed for all three modes further improve the EE of DPA by 16.43%. We also demonstrate that in contrast to the fixed power allocation (FPA) scheme, the DPA scheme increases the mode probability of NM, thereby increasing SE. Closed-form expressions are derived for the minimum average power consumption and power increments to switch to modes yielding larger throughput. We demonstrate that choosing the optimal target rate is essential to maximize the EE. Monte Carlo simulations validate the derived expressions.

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several low mobility targets. We formulate a sum-rate optimization problem that accounts for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory, and transmit beamformer for integrated sensing and communication (ISAC). Subsequently, we introduce a transfer learning based approach combining with deep deterministic policy gradient (DDPG) to enhance the overall data rate while minimizing the time it takes for users to transmit data. Additionally, we present an alternating optimization (AO) algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and offers a solution that is very close to optimal. Finally, we showcase the superiority of the proposed scheme through Monte Carlo simulations. Also, we have compared the performance with perfect channel state information (CSI) counterpart. The outcomes of simulations confirm the theoretical analysis and demonstrate the efficiency of the proposed framework. Additionally, the results reveal the advantages of incorporating HRIS aided UAV assisted ISAC in improving the quality of both communication and sensing performance.

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several moving targets. We formulate an optimization model that account for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory and transmit beamformer for integrated sensing and communication (ISAC), while aiming to maximize the overall achievable sum-rate. Subsequently, we introduce an alternating optimization algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and ultimately yielding a solution that is nearly optimal. Using Monte Carlo simulations, we showcase the superiority of the suggested approach in comparison to baseline schemes.

In this paper, we consider an ambient backscatter communication (AmBC) assisted massive machine type communication (mMTC) network comprising of an access point (AP), a cluster of backscatter devices (BDs), and a user. To achieve mMTC, non-orthogonal multiple access (NOMA) principles are used. When channel state information (CSI) knowledge is available at the AP, it is shown that the BDs can be scheduled intelligently, and the reflection coefficients of the BDs can be carefully chosen to improve the system throughput as well as energy efficiency (EE). For the first time in the literature, analytical closed-form expressions for the outage probability and throughput are derived for a NOMA signaling-based mMTC network considering Rayleigh faded channel coefficients. We confirm the accuracy of our analytical findings and showcase the advantages of the proposed scheme using Monte Carlo simulations.

This article investigates a cooperative nonorthogonal multiple access (Co-NM)-based network consisting of a multiantenna source, a full-duplex energy harvesting (EH) near user (NU) Internet of Things (IoT) node, and multiple distant user (DU) IoT nodes. The source shares a direct link to the NU, while the NU augments the harvested energy by a limited amount of its battery energy to relay the information to the selected DU. Considering time-switching (TS) or power-splitting (PS) protocol, practical nonlinear EH, successive interference cancellation error, and opportunistic Co-NM/orthogonal multiple access (OMA) (OM) switching, closed-form expressions are derived for the outage probability and throughput of both DU and NU. We demonstrate that the proposed opportunistic Co-NM/OM switching can ensure a performance similar to OM at the NU without loss in DU throughput. Also, a joint optimal choice of battery energy and PS/TS parameter helps in attaining a maximum energy efficiency (EE). Moreover, Co-NM/OM switching ensures higher EE as compared with Co-NM and OM.

In this work, we consider a full-duplex (FD) communication system that uses a simultaneous transmission and reflection (STAR) enabled reconfigurable intelligent surfaces (RIS) to assist the communication and sensing between a base station (BS) to a single set of UL and DL user, and target over the same-time frequency dimension. In order to explore the performance of the proposed framework, we offer an analytical framework and accordingly, we propose an optimization problem to jointly optimize the phase-shift matrices at the STAR RIS (S-RIS) that maximizes the possible sum-rate. Due to the non-convexity of the optimization problem, we then propose a low-complexity meta-reinforcement learning (MRL) algorithm that reduces the overall training overhead. We also demonstrate the effectiveness of the proposed algorithm in providing near-optimal design in the case of imperfect channel state information (ICSI). Additionally, in order to verify how well the proposed framework work and to show the superiority of the proposed algorithm, we provide a fair comparison with two baseline schemes a) twin delayed deep deterministic policy gradient (TD3) and b) deep deterministic policy gradient (DDPG). Simulation results verify that the proposed approach results in superior performance.

In this paper, we consider Simultaneous Transmission and Reflection (STAR) Reconfigurable Intelligent Surface (S-RIS) and passive RIS (P-RIS) assisted integrated sensing and communication system (ISAC), where S-RIS is enabled to broadcast communication signal, and P-RIS assists sensing functionalities. In particular, we jointly optimize the beamforming vector at the multi-antenna ISAC transmitter, and phase shift vector to maximize the weighted sum-rate (WSR) at the communication users while taking care of the maximum power limit at ISAC transmitter while ensuring the performance of sensing model to detect targets in its vicinity and limitations of phase and amplitude of S-RIS elements. To address the non-convexity of the above problem, we propose a low-complexity alternating optimization (AO) algorithm. Furthermore, we provide a comprehensive simulation-based graphical results to verify the viability of the proposed framework with its P-RIS assisted counterpart. Eventually, exhaustive simulation results are demonstrated to present the impact of RIS elements and the number of antennas at the ISAC transmitter. Accordingly, we illustrate the impact of S-RIS and the number of targets to highlight the trade-off between sensing and communication.

In this paper, we consider a multi-user downlink non-orthogonal multiple access (NOMA) network comprising of an access point (AP), multiple near-user (NU) nodes and multiple far-user (FU) nodes. The NUs and FUs exist in clusters of size$N$and F, respectively. Using limited channel state information (CSI) knowledge, intelligent NU and FU selection coupled with mode switching between NOMA and orthogonal multiple access (OMA) is applied to improve the system throughput and energy efficiency. Considering the case of fixed power allocation (FPA) and dynamic power allocation (DPA), closed-form throughput expressions are derived for the first time in literature with mode selection and user selection. It is seen that power allocation with very limited CSI provides huge gains in throughput and saves power at the AP, and thereby increases the energy efficiency. We validate the correctness of analytical results and the superiority of the considered scheme through Monte-Carlo simulations.

High spectral and energy efficiencies are vital for the implementation of smart cities. To this end, this article investigates the performance of an amplify-and-forward (AF) relay-aided coordinated direct and relay transmission (CDRT) protocol in a downlink nonorthogonal multiple access (NOMA)-based Internet of Things (IoT) network in which the source shares the direct as well as the relayed links to the IoT near-user (NU) and the far-user (FU). Different from the existing literature, we exploit incremental relaying (IR) along with combining at the NU (and not just at the FU) to achieve a 20% increase in FU throughput while ensuring the desired performance at NU. Considering practical imperfect successive interference cancelation (SIC), we analyze the throughput performance. Both NU and FU accrue large throughput gains, and doubling of the energy efficiency (EE) is achieved over the nonincremental AF CDRT NOMA scheme and its relayed orthogonal multiple access counterpart. We accomplish this by intelligently exploiting feedback bits in the second phase of signalling to avoid unwanted relaying, which saves energy and improves EE, which is very much required from the perspective of IoT-based energy-efficient smart cities. It is seen that the proper choice of the power allocation coefficient and the target information rate is crucial for maximizing the sum throughput and EE of the considered IoT Network. Finally, we validate the correctness of the theoretical analysis and the superiority of the proposed scheme through Monte Carlo simulations.

This paper investigates the performance of a new framework for low-outage downlink non-orthogonal multiple access (NOMA) using a coordinated direct and relay transmission (CDRT) scheme with direct links to both the near-user (NU) and the far-user (FU). Both amplify-and-forward and decode-and-forward relays are considered. In this framework, the NU combines the signals from base-station and relay at each stage of the successive interference cancellation (SIC) to attain good outage performance. For both NU and FU, the expressions for outage probability and throughput are derived in closed form. We also derive the high-SNR expressions for the outage probability to demonstrate that with the proposed framework, both users harness a diversity of two without feedback bits (this is the only framework to achieve this). We demonstrate that the choice of power allocation coefficient and target symbol rates is crucial to maximizing the NU throughput while ensuring a desired target FU throughput. We demonstrate that CDRT with the proposed framework outperforms known schemes in terms of outage probability, sum throughput, and energy efficiency. Moreover, we also show that optimal rate selection is important to maximize the energy efficiency. Monte Carlo simulations validate the accuracy of the derived analytical expressions.

This paper investigates the performance of a new coordinated direct and relay transmission (CDRT) NOMA framework in which a base station (BS) communicates directly to a near user (NU), and through an amplify-and-forward (AF) relay to a far user (FU). In the first signaling phase, the BS transmits superposed symbols to both NU and relay (R), whereas in the second phase R amplifies and re-transmits the signal from the BS. In this framework, the NU optimally combines the signals from BS and R, which gives it a significant performance advantage, and allows it to harness a diversity of two. This serves as an incentive to NU to participate in NOMA signalling to assist the FU. We derive closed form expressions for the outage probability and throughput of NU and FU. We elaborate on how the power allocation and target rates can be optimally chosen so as to maximize the NU throughput while ensuring a desired FU throughput. Computer simulation results validate the improved energy efficiency of the scheme and accuracy of the derived expressions.

This paper investigates a cooperative non-orthogonal multiple access (C-NOMA) network consisting of a base station (BS), a far user (FU) and a full-duplex (FD) near user (NU). The NU does not use its own battery energy to relay to FU, and harvests energy using SWIPT principles from the BS. In this paper we consider a more practical nonlinear energy harvesting model for the first time in such a framework. We show that use of the idealized linear EH model leads to gross over-estimation of FU performance. Considering the time-splitting (TS) energy harvesting (EH) protocol, and modelling the self-interference at the FD NU, expressions are derived for the FU and NU outage probabilities in closed-form. Further, we show that by optimal selection of the TS parameter performance of FU can be further enhanced. Computer simulations confirm the accuracy of the derived analytical expressions.

In this paper, a coordinated direct and relay transmission (CDRT) based cognitive underlay downlink nonorthogonal multiple access network is considered, in which the source serves the near user (NU) directly while a dedicated relay assists the communication to the far user (FU). To ensure good performance in a power-constrained underlay scenario, NU combines (along with successive interference cancellation) the signals from the source and the relay in this unique CDRT framework. The secondary transmitter transmits with controlled power to ensure that the interference is below the interference temperature limit of the primary user. NU and FU performance is analyzed in terms of their outage probability and throughput in closed-form. Furthermore, we investigate how the choice of target rates and power allocation parameters is crucial to attaining the best performance. Extensive computer simulations are provided to validate the accuracy of the derived results.

In this paper, we analyze the performance of an underlay downlink non-orthogonal multiple access (NOMA) network with multiple near and far-users. Different from cognitive radio (CR) inspired CR-NOMA where cognitive principles are used to guarantee performance of one selected user, all nodes here are in the underlay mode, and re-use the spectrum of the primary network. Use of NOMA is counter-intuitive in an underlay network since the transmit powers are constrained to ensure that the interference temperature limit is met. We show in this paper that switching carefully between NOMA, NOMA with relaying, and orthogonal multiple access (OMA) allows good performance to be attained in such networks. We further propose a user selection scheme, and analyze its performance. We derive approximate closed-form expressions for the near and far-user throughput. Simulations are carried out to verify the derived results.